Summary
This post introduces an analysis of a critical issue encountered while using ChatGPT for iterative document updates:
silent omission or truncation of previously existing content, without user notification or version history preservation.
Why This Matters
Such behavior undermines:
- Trust in AI-generated content workflows
- Document integrity and traceability
- Reproducibility of technical and professional documentation
GitHub Repository (Full Report)
Markdown documentation (EN/JP):
GitHub - kou-saki/chatgpt-doc-update-omission-issues: Analysis and proposals regarding issues of data omission, structural loss, and silent overwrites when updating documents via ChatGPT dialogue. Includes bilingual documentation (JP/EN).
Includes:
- Step-by-step breakdown of the issue
- Structural and architectural hypotheses
- Suggestions for protocol-level improvement
- Reproduction examples and version control observations
Context
The findings are based on actual conversations with ChatGPT (“Mike”) and highlight limitations in the current document update model.
They may be of particular interest to:
- AI product designers
- Users relying on long-term document editing with LLMs
- Researchers working on AI alignment, version control, and trust
I welcome comments, reproductions, technical critiques, and discussions on how to improve document fidelity in inference-based AI systems.